Total Crime Research Articles

Effect of police expenditure on crime rate in India

Purpose This study aims to examine the relationship between law enforcement expenditures, economic indicators and various crime rates in India using data from the National Crime Records Bureau (NCRB) spanning from 2001 to 2021. Design/methodology/approach The study uses data sourced from the NCRB covering violent crimes, property crimes, economic crimes and total crimes under the Indian Penal Code across 27 states and 2 union territories in India. Crime rates per 100,000 people are calculated using projected population figures from the 2001 and 2011 censuses, adjusting for census periods and growth rates. Economic variables such as per capita real gross state domestic product (GSDP) and total police expenditure are obtained from state government sources, adjusted to constant prices. Pooled ordinary least squares analysis is conducted to explore the relationships between crime rates, conviction rate, economic factors and police expenditure over the three-time points: 2001, 2011 and 2021. Findings The study reveals a complex relationship between police expenditure and crime rates across different categories in India. Increased police expenditure is positively associated with higher reporting rates for violent and economic crimes, suggesting that enhanced law enforcement resources may improve crime detection and reporting. However, contrary to some expectations, higher police spending does not necessarily lead to a reduction in overall crime rates. Economic prosperity, measured by per capita GSDP, is found to reduce violent crime rates but is associated with an increase in property and economic crimes, likely due to the greater opportunities for such crimes in wealthier regions. Research limitations/implications The study’s reliance on state-level aggregate data and NCRB crime statistics, which are based on reported incidents, might not fully capture the true extent of criminal activity, particularly in regions with underreporting issues. In addition, the use of projected population data beyond 2001 and 2011 introduces some uncertainty into the analysis. Future research should explore district-level data to better understand regional variations in crime and incorporate social, cultural and innovative policing strategies to provide a more comprehensive analysis. Practical implications Policymakers should recognize the nuanced impacts of police expenditure and economic prosperity on crime rates. While increased law enforcement resources may enhance crime reporting, they do not guarantee a reduction in overall crime rates. Economic growth, while beneficial in reducing violent crimes, may inadvertently increase property and economic crimes due to greater opportunities for criminal activity. These insights suggest the need for targeted interventions that consider both economic factors and law enforcement strategies in crime prevention efforts. Originality/value This study contributes to the ongoing debate about the effectiveness of police expenditure in reducing crime by highlighting its limited impact on overall crime rates in India. The findings underscore the importance of understanding the diverse effects of economic prosperity on different crime types, offering valuable insights for policymakers aiming to design evidence-based crime prevention strategies that account for socioeconomic disparities and regional contexts.

Association of Involuntary Displacement of People Experiencing Homelessness and Crime in Denver, CO: A Spatiotemporal Analysis.

In 2022, approximately 580,000 people experienced homelessness in the United States. In response, many cities have implemented "camping ban" policies enforced by involuntary displacement of homeless encampments. Displacement has been cited as a strategy to protect public health and safety. However, there is mixed evidence that displacement is effective in reducing crime, while it is associated with other adverse health outcomes. To evaluate the neighborhood-level association between displacement and crime, we performed a retrospective (November 2019 to July 2023) pre-post spatiotemporal analysis using administrative data from Denver, CO. We used the Knox test statistic to detect excess clustering and change in total crime, as well as crime stratified by the National Incident-Based Reporting System (NIBRS) category, within spatiotemporal proximity to displacement events. We found that, on average, clustering of crime is high both before and after displacement. Within a 0.25-mile radius, displacement is associated with a statistically significant but modest decrease in crime, between - 9.3% within 7days (p < 0.001) and - 3.9% within 21days (p = 0.002). We found no consistent change in composite crime at a 0.5- or 0.75-mile radius. Hyperlocal decreases were driven by significant decreases in public disorder and auto theft, while crimes against persons increased and displayed high clustering post-displacement. There were no changes in any other offense type. Involuntary displacement is not consistently associated with changes in clustering of crime and may exacerbate violence in nearby areas.

Macroeconomic Determinants of Crime against Children A Case Study of Punjab

Despite it being a growing cause of concern, the relationship between macroeconomic indicators and crime against children has previously remained widely unnoticed. An extensive range of literature is reviewed that gives this domain of research a different perspective by investigating crime against children existent in various countries around the world. The analysis of this paper is divided into three sections. Firstly, a regression analysis has been run using time series data taken from Punjab, Pakistan for the years 2007-2017. Applying the Ordinary Least Square Technique and using income, unemployment, and education as independent variables and crime rate against female children as the dependent variable; a regression model was run. While income and education show a statistically significant relationship, a prediction cannot be made due to a limited data availability of the crime variable. Secondly, this paper takes a descriptive approach by using a correlation matrix to observe the relationship between total crime rate against male children and unemployment, poverty and social sector spending. Results of this Matrix indicate a positive relationship between crime with unemployment and crime with poverty, while there is a negative linkage among social sector spending and crime. However, a correlation matrix can only be used to define a causal relationship between variables and not be employed to make reliable cause and effect estimations.

The relationships between Urban Tree Canopy Cover and Crime in São Paulo City, Brazil

Prior research has indicated lower crime rates in areas with greater tree canopy cover predominantly in the Global North. There are few studies from the Global South, and more specifically in Latin America. Given the high prevalence of crime in Latin American cities, the need to address social and environmental inequalities using nature-based solutions is urgent. This study examines crime and tree canopy cover through a series of spatial analyses for São Paulo city, Brazil, while controlling for potential confounders. We tested six levels of data aggregation that were combined with three different crime types and two denominators of crime, to account for spurious findings from Modifiable Areal Unit Problems (MAUP). Of the 36 models, a majority of 27 models (75 %) indicate a negative relationship between crime and tree cover canopy, while 4 models (11 %) show a positive correlation, and 5 models (14 %) suggest no statistically significant relationship. Our best models allow us to infer that 10 % greater tree canopy cover is associated with a 1.20 % fewer property, personal and total reported crimes, with statistical significance at the p < 0.001 level. These findings support our hypothesis that there is a negative relationship between tree canopy cover and crime, with a stronger association observed for property crime than personal or total crime. The negative relationship persisted independently of the level of data aggregation, crime types, crime denominator and spatial models specifications (lag and SARMA), avoiding statistical bias of MAUP. The results follow the literature that have observed tree cover associated with lower crime rates, for different types of crime, while adding a new climatic and cultural context to the evidence base.

DEMOGRAPHIC DYNAMICS AND URBAN PROPERTY CRIME: A LINEAR REGRESSION ANALYSIS IN KUALA LUMPUR AND PUTRAJAYA (2015-2020)

In an era where urbanization is rapidly transforming the landscape of cities, this study provides a crucial overview of how demographic shifts influence property crime in metropolitan areas. The paper delves into the complex interplay between property crime rates and demographic characteristics in the urban settings of Kuala Lumpur and Putrajaya, Malaysia, over six years from 2015 to 2020. Employing linear regression analysis, the study meticulously examines the relationship between property crime and various demographic factors, including total population, male and female populations, and residential and household densities. The findings indicate a consistent positive correlation between total population and property crime, emphasizing urban density's role in crime propensity. Notably, the male population shows a stronger correlation with property crime than females. The study also highlights how residential and household densities influence property crime in these urban settings. These insights are invaluable for policymakers and urban planners, guiding targeted strategies to reduce property crime in growing cities.

Neighborhood Deprivation and Crime on Asthma Exacerbations and Utilization in Mild Adult Asthma

Social determinants of health (SDOH) have a significant impact on asthma outcomes, with factors such as income level and neighborhood environment playing crucial roles. This study aimed to assess the impact of Neighborhood Deprivation Index (NDI) and Total Crime Index (TCI) on acute asthma exacerbation (AAE) and asthma-related Emergency Department and urgent care (ED/UC) visits in adults with mild asthma. This retrospective cohort study utilized administrative data from Kaiser Permanente Southern California among 198,873 mild adult asthma patients between January 1, 2013, and December 31, 2018. Robust Poisson regression models, adjusted for age and sex, were employed to investigate the associations of NDI and TCI with AAE and asthma-related ED/UC visits. Data analysis included subgroup assessments by race and ethnicity and BMI categories to explore potential disparities in asthma outcomes. Among the cohort, 12,906 patients (6.5%) experienced AAE in one year, with Black patients having the highest AAE percentage (7.1%). Higher NDI quintiles were associated with increased AAE risk (aRRs: 1.11-1.27), with similar trends across BMI categories and race or ethnicity, except for Black patients. TCI showed weaker associations with AAE. Regarding ED/UC visits, 5.0% had such visits within one year. Higher NDI quintiles were associated with higher ED/UC visit risk (aRRs: 1.23-1.75), while TCI associations were weaker. Addressing socioeconomic disparities, as indicated by NDI, may be crucial in mitigating asthma exacerbations and reducing healthcare utilization, highlighting the importance of incorporating social determinants into asthma management strategies even in patients with mild asthma.

The Road to Crime: An Unintended Consequence of the Interstate Highway System

Abstract The necessity to overhaul national infrastructure has become one of the key issues of US domestic policy in recent years. While the benefits of infrastructure spending are often straightforward, its unintended consequences are less well understood. This paper studies the impact of transportation infrastructure on local crime, focusing on the construction of the Interstate Highway System (IHS), the largest public works project in US history. Using a staggered difference-in-differences design and a county-by-year panel dataset spanning all US counties between 1960 and 1993, we find that a highway opening in a county led to an 8% rise in total index crime. This effect is driven by property crime (burglary, larceny, and motor vehicle theft), although some evidence suggests an increase in violent crime as well. Exploring potential mechanisms, we show that the positive effect of highways on crime is concentrated in counties with low pre-existing stock of police resources where increased connectivity may have raised the returns to crime. Moreover, we find that highway construction induced higher local employment, average firm size, and the local population size, thus increasing the returns to criminal activity, and ultimately the risk of victimisation in the newly-connected counties.

Reconceptualizing Policing for Cybercrime: Perspectives from Singapore

As cybercrime proliferates globally, law enforcement agencies face significant challenges in responding effectively. This essay shares perspectives from Singapore, where cybercrime accounted for about 70% of the total annual crime in 2023, with no clear data on case resolution rates. This situation reflects a broader global trend and highlights the need to reconceptualize policing objectives in cyberspace. The fundamental differences between cybercrime and physical crime necessitate a shift from emphasizing the identification and prosecution of perpetrators to adopting a harm-centric perspective. Under this perspective, structures and policies should be implemented to disrupt financial flows, ensure data security, disrupt the spread of harmful content, and prevent physical damage. Once this is done, strategies such as public–private partnerships, international cooperation, and training and building capabilities to address specific harms can be more effectively implemented to mitigate the growing threat that cybercrime poses worldwide.

AN EXPLORATORY STUDY ON CRIME AGAINST WOMEN IN STATES OF WESTERN AND NORTHERN REGIONS OF INDIA

The paper discussed the crimes against women in states of western and northern regions of India. It has found that Uttar Pradesh, Rajasthan and Maharashtra has higher incidences of crimes against women then other states in the regions. Goa, Himachal Pradesh and Uttarakhand has comparatively lower incidences of crimes against women. Uttar Pradesh has the highest incidences and victims of SLL crimes against women, followed by Maharashtra and Rajasthan On the other hand, least incidences and victims of such crimes is reported in Goa preceded by Himachal Pradesh and Punjab. Uttar Pradesh also has the highest incidences and victims of total crime against women (IPC+SLL) followed by Maharashtra and Rajasthan. On the other hand, least incidences and victims of total crime against women (IPC+SLL) reported in Goa preceded by Himachal Pradesh and Uttarakhand. Therefore, Governments of the concern states should implement proper policies and regulations for minimizing the incidences of crime against women.

Describing the geographical linkage between the UK Millennium Cohort Study and crime incidents in England and Wales

BackgroundEnhancing longitudinal cohort studies by linking routine external data to them is increasingly used to evaluate how local environments impact participants' outcomes (e.g. crime on adolescents' perception of security and victimisation). ObjectiveTo describe the geographical linkage between the UK Millennium Cohort Study (MCS) and street-level crime incidents reported to the Police in England and Wales, and to estimate crime count and rates around MCS participants' residences. MethodsEight years of monthly street-level police data were linked to the residential postcodes of MCS participants living in England and Wales in surveys 5, 6 and 7 to create individual-level variables of neighbourhood crime counts and rates (28,724 surveys and 11,365 individuals). Radial buffers around participants' residences were created at ages 11, 14 and 17. Crime counts and rates were created prior to the month of interview (at 1, 3, 6, 9, and 12 months prior). A homogenisation of crime categories reported in the police data was conducted to evaluate changes over time and areas. Multivariate models were used to study the association between MCS participants' demographic characteristics and derived measures of neighbourhood crime. ResultsWhile total crime rates and counts around MCS participants remain stable over the period, they hide heterogeneous upward and downward trends in specific sub-categories, with violence and sexual offences showing a larger increase. We observe a negative socioeconomic gradient between household income deciles, recorded at age 11, and subsequent exposure to neighbourhood crime. ConclusionLinking routine crime data to longitudinal studies, such as the MCS, which follow children and their families through a critical period of development, can provide a new resource to understand how local crime impacts child and adolescent outcomes.

Do progressive prosecutors increase crime? A quasi‐experimental analysis of crime rates in the 100 largest counties, 2000–2020

AbstractResearch summaryIn recent years, there has been a rise in so‐called “progressive prosecutors” focused on criminal justice reforms. Although there has been considerable debate about the relationship between progressive prosecution policies and crime rates, there has been surprisingly little empirical research on the topic. Building on the limited extant research, we examined whether the inauguration of progressive prosecutors in the nation's 100 most populous counties impacted crime rates during a 21‐year period (2000 to 2020). After developing an original database of progressive prosecutors in the 100 largest counties, we used heterogeneous difference‐in‐differences regressions to examine the influence of progressive prosecutors on crime rates. Results show that the inauguration of progressive prosecutors led to statistically higher index property (∼7%) and total crime rates (driven by rising property crimes), and these effects were strongest since 2013—a period with an increasing number of progressive prosecutors. However, violent crime rates generally were not higher after a progressive prosecutor assumed control.Policy implicationsDespite concerns that the election of progressive prosecutors leads to “surging” levels of violence, these findings suggest that progressive‐oriented prosecutorial reforms led to relatively higher rates of property crime but had limited impact on rates of violent crime. In fact, in absolute terms, crime rates fell in jurisdictions with traditional and progressive prosecutors. Yet, relative property crime rates were greater after the inauguration of progressive prosecutors. Given that prior research shows progressive prosecutors reduce mass incarceration and racial inequalities, our findings indicate that higher property crime rates may be the price for these advancements.

A location discrete choice model of crime: Police elasticity and optimal deployment.

Despite the common belief that police presence reduces crime, there is mixed evidence of such causal effects in major Latin America cities. In this work we identify the casual relationship between police presence and criminal events by using a large dataset of a randomized controlled police intervention in Bogotá D.C., Colombia. We use an Instrumental Variables approach to identify the causal effect of interest. Then we consistently estimate a Conditional Logit discrete choice model with aggregate data that allow us to identify agents' utilities for crime location using Two Stage Least Squares. The estimated parameters allow us to compute the police own and cross-elasticities of crime for each of the spatial locations and to evaluate different police patrolling strategies. The elasticity of crime to police presence is, on average across spatial locations, -0.26 for violent crime, -0.38 for property crime and -0.38 for total crime, all statistically significant. Estimates of cross-elasticities are close to zero; however, spillover effects are non-negligible. Counterfactual analysis of different police deployment strategies show, for an optimal allocating algorithm, an average reduction in violent crime of 7.09%, a reduction in property crimes of 8.48% and a reduction in total crimes of 5.15% at no additional cost. These results show the potential efficiency gains of using the model to deploy police resources in the city without increasing the total police time required.

COVID-19, Race, and Crime: An Early Look at Racial Disparities in U.S. Arrest Data throughout the COVID-19 Pandemic

Background: This study explores how arrests changed in response to the COVID-19 pandemic across race. Daily life changed for everyone across the country with the onset of the pandemic, and early works have shown that crime changed in this period. Method: Official arrest data were pulled from the Indiana State Police database for several violent and property crimes covering 26 counties. Data were gathered from 2017 to 2021 for a comparison of pre-COVID-19 versus after the onset of COVID-19 (2020–2021). An OLS regression was run to assess differences in these patterns of arrests across Black and White populations. Results: This analysis finds that Black homicide, White homicide, and total Black violent crime arrests were significantly related to COVID-19 measures after controlling for other variables. The COVID-19 measures indicate that these crimes saw an increase in arrest after the onset of the pandemic and that these effects may not have been identical across race. Conclusions: The COVID-19 pandemic was linked to crime across race in the state of Indiana. Moving forward, it is important to uncover how crime changed across race in other locales and exactly what mechanisms may have driven these changes.

Predicting and analysing initiator crime environments based on machine learning for improving urban safety

PurposeInitiator crimes, also known as near-repeat crimes, occur in places with known risk factors and vulnerabilities based on prior crime-related experiences or information. Consequently, the environment in which initiator crimes occur might be different from more general crime environments. This study aimed to analyse the differences between the environments of initiator crimes and general crimes, confirming the need for predicting initiator crimes.Design/methodology/approachWe compared predictive models using data corresponding to initiator crimes and all residential burglaries without considering repetitive crime patterns as dependent variables. Using random forest and gradient boosting, representative ensemble models and predictive models were compared utilising various environmental factor data. Subsequently, we evaluated the performance of each predictive model to derive feature importance and partial dependence based on a highly predictive model.FindingsBy analysing environmental factors affecting overall residential burglary and initiator crimes, we observed notable differences in high-importance variables. Further analysis of the partial dependence of total residential burglary and initiator crimes based on these variables revealed distinct impacts on each crime. Moreover, initiator crimes took place in environments consistent with well-known theories in the field of environmental criminology.Originality/valueOur findings indicate the possibility that results that do not appear through the existing theft crime prediction method will be identified in the initiator crime prediction model. Emphasising the importance of investigating the environments in which initiator crimes occur, this study underscores the potential of artificial intelligence (AI)-based approaches in creating a safe urban environment. By effectively preventing potential crimes, AI-driven prediction of initiator crimes can significantly contribute to enhancing urban safety.

Did the association between alcohol outlet density and crime change during COVID shelter-in-place orders?

We investigated whether greater concentrations of on- and off-sale alcohol outlets were associated with crime and whether this association was moderated by COVID-19 shelter-in-place orders (SIP) that restricted on-premises consumption of alcohol. Crimes (2019-2020) and addresses of licenced alcohol outlets in a medium-sized California city were geocoded within census block groups (N = 61). On- and off-sale alcohol outlet density was calculated as licenced outlets/2.59 km2 (1 square mile). Multilevel negative binomial regression analyses were conducted to examine associations between alcohol outlet density and crime, and possible moderating effects of SIP, controlling for block group demographic characteristics and density of other retail businesses. On-sale outlet density was positively associated with total crimes and Part 2 crimes, while off-sale outlet density was inversely associated with total crime and Part 2 crimes. Overall, SIP was not significantly associated with crime, but moderated the associations of on-sale density with total crime and Part 1 crimes such that reductions in crime during SIP were observed in higher density areas. The association of off-sale outlets with crime was not moderated by SIP policies. On-sale outlet density, but not off-sale density, appears to be associated with increased crime. The results further indicate that restrictions in hours and service imposed by SIP policies reduced crime in high on-sale outlet density areas. These findings reinforce the importance of regulating alcohol outlet density and hours of service, especially for on-sale outlets, as a crime reduction strategy.

Unraveling the influence of income-based ambient population heterogeneity on theft spatial patterns: insights from mobile phone big data analysis

While previous research has underscored the profound influence of the ambient population distribution on the spatial dynamics of crime, the exploration regarding the impact of heterogeneity within the ambient population, such as different income groups, on crime is still in its infancy. With the support of mobile phone big data, this study constructs an index of ambient population heterogeneity to represent the complexity of the social environment. After controlling for the effects of total ambient population, nonlocal rate, transportation accessibility, crime attractors, and crime generators, this study employs a negative binomial regression model to examine the influence of ambient population heterogeneity and different income groups on the spatial manifestations of thefts. The findings indicate that ambient population heterogeneity significantly escalates the incidence of thefts, with middle and upper-middle-income groups acting as more attractive targets, whereas the higher-income group exerts a deterrent effect. The interaction analysis shows that increased population heterogeneity contributes to social disorder, thereby amplifying the attractiveness of the ambient population to perpetrators. These conclusions highlight the crucial role of ambient population heterogeneity in explaining crime dynamics and therefore enrich the routine activity theory.

수도권 신도시의 범죄 현황 및 영향요인 분석

The purpose of this study is to analyze the status of crime in new town in the Capital Region of Korea and analyze the physical factors that affect crime. The spatial scope of the study is Bundang, Ilsan, Dongtan, and Pangyo, which are representative of the first and second new cities, and the temporal range was set from 2010 to 2014, and after analyzing the crime status of four new cities, the characteristics of major hotspots were confirmed through field surveys from a micro perspective, and the factors influencing crime were identified through spatial regression analysis from a macro perspective.&#x0D; The main results obtained through this study are as follows. First, as a result of the analysis of the crime status of the new city, it was found that the rate of theft and violence was high in the first new city. In particular, theft crimes occur relatively frequently, and sex crimes occur less frequently, but they have been confirmed to occur steadily. The second new city varies greatly from region to region, with relatively many violent crimes occurring in general, and the incidence of violent crimes such as sex crimes and robbery crimes was relatively higher than that of the first new city.&#x0D; Based on the analysis of the five major crime hotspots, the crime impact factors from a micro perspective were analyzed through field surveys at major points, but the use of buildings in the area was similar to commercial facilities, apartments, multi-family and multi-family houses, row houses, and multi-use facilities.&#x0D; As a result of analyzing crime impact factors from a macro perspective through spatial regression analysis in Bundang New Town, it was found that other housing (+), entertainment/lodging (+), exercise facilities (+), and CCTV (+) had the most significant impact on total crime. Theft was the most significant variable for other dwellings (+), entertainment/lodging (+), and exercise facilities (+). Single-family houses (+), general restaurants (+), exercise facilities (+), and CCTV (+) had the most significant impact on violence. Finally, single-family houses (+), general restaurants (+), exercise facilities (+), and CCTV (+) had the most significant impact on sex crimes.

A Bayesian Analysis of a Cognitive-Behavioral Therapy Intervention for High-Risk People on Probation.

This analysis employs a Bayesian framework to estimate the impact of a Cognitive-Behavioral Therapy (CBT) intervention on the recidivism of high-risk people under community supervision. The study relies on the reanalysis of experimental datal using a Bayesian logistic regression model. In doing so, new estimates of programmatic impact were produced using weakly informative Cauchy priors and the Hamiltonian Monte Carlo method. The Bayesian analysis indicated that CBT reduced the prevalence of new charges for total, non-violent, property, and drug crimes. However, the effectiveness of the CBT program varied meaningfully depending on the participant's age. The probability of the successful reduction of drug offenses was high only for younger individuals (<26years old), while there was an impact on property offenses only for older individuals (>26years old). In general, the probability of the successful reduction of new charges was higher for the older group of people on probation. Generally, this study demonstrates that Bayesian analysis can complement the more commonplace Null Hypothesis Significance Test (NHST) analysis in experimental research by providing practically useful probability information. Additionally, the specific findings of the reestimation support the principles of risk-needs responsivity and risk-stratified community supervision and align with related findings, though important differences emerge. In this case, the Bayesian estimations suggest that the effect of the intervention may vary for different types of crime depending on the age of the participants. This is informative for the development of evidence-based correctional policy and effective community supervision programming.

Wheeling into school and out of crime: Evidence from linking driving licenses to minimum academic requirements

“No Pass No Drive” (NPND) laws deny or revoke driver's licenses to minors if they drop out of school, are frequent truants, show behavioral problems, or have a low academic performance. Using Federal Bureau of Investigation (FBI) arrest data, we exploit state, time, and cohort variation to show that NPND laws are associated with a significant decline in total crime, DUI and property crimes among 16 to 18 year old males, and in DUIs among 16 to 18 year old females. We argue that NPND laws are a relatively low cost policy alternative to compulsory attendance laws that increase education and generate positive externalities. To explore unreported in-school crimes, we use micro data from the Youth Risk Behavior Survey (YRBS) and find that NPND laws decrease in-school risky activities. However, we also find that they have unintended consequences for in-school risky behavior when accompanied with strict academic or behavioral requirements.

Türkiye’de Ekonomik Suçların Dinamikleri ve Belirleyicileri

The economics of crime deals with two issues: (i) the economic cost of the crime, and (ii) the economic motivations behind the crime that is committed by individuals. In this context, crime is distinguished by whether it has an economic characteristic or not. Economic crimes can be defined as non-violent crimes committed to gain profit, benefit, or social status. Understanding the dynamics of crime is crucial for policy-making to minimize the economic and social costs to society. In the last decade, the total amount of crime has been increasing continuously in Türkiye. The economic crimes accounted for approximately 60 % of total crimes until 2012. In the later years, the rate of other crimes (particularly, assault, threat, and traffic crimes) in total crimes have surpassed economic crimes, nonetheless, the number of economic crimes has also been increasing gradually and peaked in 2018. Moreover, economic crimes show regional differences in Türkiye, and revealing these disparities may provide useful insights to policymakers. Therefore, the examination of the dynamics of economic crimes at the regional level and its relationship with economic variables in Türkiye seems to be timely and important. We collect a broad economic crime data of 26 sub-regions for the 2006-2020 period. We first analyze the regional differences in property and fraud by utilizing the mapping method. We also tried to reveal the link between income, unemployment, and education level with property and fraud with dynamic panel methodology and revealed that variables have different effects on different economic crimes.